P1508: GUIDE PERSONALIZED ANTIBIOTIC STEWARDSHIP FOR PSEUDOMONAS AERUGINOSA BLOODSTREAM INFECTIONS IN AML PATIENTS: BASED ON A MACHINE LEARNING MODEL
Authors: Xiaomeng Feng, Sizhou Feng
Journal: HemaSphere (2023)

Topic: 30. Infections in hematology (incl. supportive care/therapy) Background:Pseudomonas aeruginosa (PA) is a predominant organism with complicated resistance mechanism within the hospital environment. Nearly all PA infections are associated with compromised host defenses, especially in hematological malignancies like acute myeloid leukemia (AML), who may have the most potential to benefit from antibiotic stewardship. Aims: We included the AML complicated with PA BSI specifically to predict the mortality or recurrence rate by machine learning (ML) and try to define the appropriate way of antibiotic treatment individually. Methods: ML model was developed to predict the likelihood on mortality within 30days and target the most appropriate antibiotic regimen and duration. In the observation of recurrence, all subsequent PA culture were reviewed independently by two physicians to check for true infection or colonization. Local institutional review boards approved all study procedures, and all participants provided informed consent. Results: 277 AML patients with BSIs due to PA from Jan, 2014 to Dec, 2022 were included in the final cohort. The overall 30-day mortality was 12.6%. We developed a ML model including 11 features (age, total bilirubin, stage, IET48h, pneumonia, peritonitis, prolonged neutropenia, cefepime MIC, meropenem MIC, piperacillin/tazobactam MIC, levofloxacin MIC) selected by the RFECV model after balancing the data by SMOTE algorithm. In the training cohort, the model had an AUC value of 0.893 (accuracy 0.727, specificity 0.889, sensitivity 0.778). The corresponding model performance measures in the validation cohort were comparable to those of the training cohort: AUC value of 0.826(accuracy 0.788, specificity 0.746, sensitivity 0.827). The patients were divided into low risk, moderate risk, and high risk with 30d mortality rate 2.38%, 53.50%, 92.80% respectively. Among 69 patients in the high-risk group, it seems that combination therapy improved survival than monotherapy, while the p-value did not show statistical significance (p=0.480). And long-course treatment is superior than short-course treatment on 30d mortality (p=0.047), while no statistical significance in the low and moderate risk group. 13.6% of patients in the high-risk group experienced recurrence with short course treatment and no recurrence with long course treatment (p=0.232) when the recurrence rate within 90 days after antibiotic interruption was set as the study endpoint. And in consolidation populations, there are no significant difference in 90d recurrence among the three groups including 3-7d (n=71), 8-10d (n=37) and 11-21d treatment (n=32) (p=0.332). Our study also found that MDRPA and CRPA had the highest detection rates in the salvage population, at 17.9% and 28.2%, and the lowest detection rate of resistant bacteria in the consolidation population, at 7.5% and 11.6%, respectively (p=0.002 and p=0.035). There is no statistical difference in these populations when comparing the antibiotic exposure (Fluoroquinolone, Piperacillin-tazobactam, Carbapenem, Cephalosporin) (p=0.151, p=0.614, p=0.808, p=0.386). And the total bilirubin values were higher in MDRPA populations. Summary/Conclusion: We included the dual factors of host and pathogen through machine learning model to screen out the high-risk population of AML patients. The combination therapy and long-course antibiotic treatment were more efficient in this population, which provided evidence-based medical evidence for the clinical practice of whole-course antibiotic stewardship in AML patients complicated with bacteremia.Keywords: Infection, AML, Immune deficiency